Senior Data Quality & Observability Architect

McKesson Medical-Surgical Inc.; United States; À distance; USD 105 000 - 174 000; Plein temps

Résumé du poste

McKesson is seeking a Senior Data Quality Analyst to build and mature a data quality capability across data platforms. You will work with Data Domain Officers, stakeholders, data teams, and contractors to translate business expectations into practical standards, rules, dashboards, and services implemented across our data platforms and business lines.

This highly visible role requires collaboration, creativity, and the ability to navigate ambiguity while delivering scalable data quality solutions

Qualifications

• Bachelor's degree in Information Technology, Computer Science, Business Administration, or a related field (Master's degree preferred) or related experience.
• Typically requires 7+ years of experience in data quality, data observability, data engineering, or data governance, including direct experience building a data quality capability within a large organization.
• Proven experience designing data quality frameworks and implementing data observability tooling and practices.
• Strong understanding of data pipelines, data architecture, and how quality and observability checks integrate into ETL/ELT and streaming workflows.
• Hands-on experience with data quality/observability platforms (Collibra, Informatica, Monte Carlo, Great Expectations, Soda) and catalog/lineage tools (Alation, Informatica Axon)
• Proficiency with SQL and at least one scripting language (Python preferred) for building and automating quality checks.

Responsabilités

• Design and build the enterprise data quality framework - dimensions, scoring methodology, thresholds, and escalation paths - so that quality is measured consistently across domains.
• Stand up data observability capability (freshness, volume, schema drift, distribution, lineage-aware anomaly detection) across priority pipelines and datasets, so quality issues surface before they reach consumers
• Define and operationalize data quality rules and checks for critical data elements, working with data owners and stewards to encode business meaning into testable logic
• Build the data quality service layer: reusable rule libraries, monitoring dashboards, and alerting that downstream teams can plug into rather than rebuild
• Conduct data quality assessments, triage root causes (source, pipeline, model, or definitional), and drive remediation plans with data owners and engineering teams through to closure
• Evaluate, implement, and administer data quality and observability tooling (e.g., Collibra, Informatica, Monte Carlo, Great Expectations) as part of the broader data catalog and lineage stack
• Establish data quality SLAs/SLOs with data owners and platform teams, and report quality and observability KPIs to track the health of the capability over time
• Partner with legal, compliance, and privacy teams to ensure quality controls meet regulatory requirements (HIPAA, GDPR, CCPA) without duplicating existing governance processes.
• Train data stewards and business users on the data quality framework, tooling, and how to interpret observability signals and elevate issues
• Act as the subject matter expert on data quality and observability practices, advising stakeholders on how to design new systems and pipelines with quality built in
• Proactively identify systemic data quality risks and elevate capability gaps to leadership

Connaissances

SQL

Python

Formation

Bachelor's degree in IT/CS/Business (or related)

Master's degree preferred

Outils

Collibra

Informatica

Monte Carlo

Great Expectations

Soda

Alation

Informatica Axon

Description du poste

McKesson is seeking a Senior Data Quality Analyst to build and mature a data quality capability across data platforms. You will work with Data Domain Officers, stakeholders, data teams, and contractors to translate business expectations into practical standards, rules, dashboards, and services implemented across our data platforms and business lines.

This highly visible role requires collaboration, creativity, and the ability to navigate ambiguity while delivering scalable data quality solutions